111 research outputs found
Design of a monitor for the debugging and development of multiprocessing process control systems : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Computing Technology at Massey University
This thesis describes the design of a general purpose tool for debugging and developing multimicroprocessor process control systems. With the decreasing pnce of computers, multimicroprocessors are increasingly being used for process control. However, the lack of published information on multiprocessing systems and distributed systems has meant that methodologies and tools for debugging and developing such systems have been slow to develop. The monitor designed here is system independent, a considerable advantage over other such tools that are currently available
Disentanglement of Latent Representations via Sparse Causal Interventions
The process of generating data such as images is controlled by independent
and unknown factors of variation. The retrieval of these variables has been
studied extensively in the disentanglement, causal representation learning, and
independent component analysis fields. Recently, approaches merging these
domains together have shown great success. Instead of directly representing the
factors of variation, the problem of disentanglement can be seen as finding the
interventions on one image that yield a change to a single factor. Following
this assumption, we introduce a new method for disentanglement inspired by
causal dynamics that combines causality theory with vector-quantized
variational autoencoders. Our model considers the quantized vectors as causal
variables and links them in a causal graph. It performs causal interventions on
the graph and generates atomic transitions affecting a unique factor of
variation in the image. We also introduce a new task of action retrieval that
consists of finding the action responsible for the transition between two
images. We test our method on standard synthetic and real-world disentanglement
datasets. We show that it can effectively disentangle the factors of variation
and perform precise interventions on high-level semantic attributes of an image
without affecting its quality, even with imbalanced data distributions.Comment: 16 pages, 10 pages for the main paper and 6 pages for the supplement,
14 figures, submitted to IJCAI 2023. V2: added link to repositor
Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis
An indigenous perspective on the effectiveness of debiasing techniques for
pre-trained language models (PLMs) is presented in this paper. The current
techniques used to measure and debias PLMs are skewed towards the US racial
biases and rely on pre-defined bias attributes (e.g. "black" vs "white"). Some
require large datasets and further pre-training. Such techniques are not
designed to capture the underrepresented indigenous populations in other
countries, such as M\=aori in New Zealand. Local knowledge and understanding
must be incorporated to ensure unbiased algorithms, especially when addressing
a resource-restricted society.Comment: accepted with invite to presen
Poison is Not Traceless: Fully-Agnostic Detection of Poisoning Attacks
The performance of machine learning models depends on the quality of the
underlying data. Malicious actors can attack the model by poisoning the
training data. Current detectors are tied to either specific data types,
models, or attacks, and therefore have limited applicability in real-world
scenarios. This paper presents a novel fully-agnostic framework, DIVA
(Detecting InVisible Attacks), that detects attacks solely relying on analyzing
the potentially poisoned data set. DIVA is based on the idea that poisoning
attacks can be detected by comparing the classifier's accuracy on poisoned and
clean data and pre-trains a meta-learner using Complexity Measures to estimate
the otherwise unknown accuracy on a hypothetical clean dataset. The framework
applies to generic poisoning attacks. For evaluation purposes, in this paper,
we test DIVA on label-flipping attacks.Comment: 8 page
Challenges in Annotating Datasets to Quantify Bias in Under-represented Society
Recent advances in artificial intelligence, including the development of
highly sophisticated large language models (LLM), have proven beneficial in
many real-world applications. However, evidence of inherent bias encoded in
these LLMs has raised concerns about equity. In response, there has been an
increase in research dealing with bias, including studies focusing on
quantifying bias and developing debiasing techniques. Benchmark bias datasets
have also been developed for binary gender classification and ethical/racial
considerations, focusing predominantly on American demographics. However, there
is minimal research in understanding and quantifying bias related to
under-represented societies. Motivated by the lack of annotated datasets for
quantifying bias in under-represented societies, we endeavoured to create
benchmark datasets for the New Zealand (NZ) population. We faced many
challenges in this process, despite the availability of three annotators. This
research outlines the manual annotation process, provides an overview of the
challenges we encountered and lessons learnt, and presents recommendations for
future research.Comment: Accepted in Ethics and Trust in Human-AI Collaboration:
Socio-Technical Approaches @ The 32nd International Joint Conference on
Artificial Intelligenc
Prevalence and architecture of de novo mutations in developmental disorders.
The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year
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The contribution of X-linked coding variation to severe developmental disorders
Abstract: Over 130 X-linked genes have been robustly associated with developmental disorders, and X-linked causes have been hypothesised to underlie the higher developmental disorder rates in males. Here, we evaluate the burden of X-linked coding variation in 11,044 developmental disorder patients, and find a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We develop an improved strategy to detect X-linked developmental disorders and identify 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known developmental disorder-associated genes. Importantly, we estimate that, in male probands, only 13% of inherited rare missense variants in known developmental disorder-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders
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